LB005KIDNEY IMPLICATIONS OF THE INITIAL EGFR RESPONSE TO SGLT2 INHIBITION WITH EMPAGLIFLOZIN: THE ‘EGFR DIP’ IN EMPA-REG OUTCOME
Notice bibliographique
Résumé
Abstract Background and Aims Empagliflozin (EMPA) reduces cardiovascular and renal risk in patients with type 2 diabetes (T2D) and established cardiovascular disease (CVD). EMPA induces an initial ‘dip’ in estimated glomerular filtration rate (eGFR). Although considered to be of haemodynamic origin and largely reversible, this needs to be better understood. We investigated whether the initial eGFR dip after EMPA initiation was influenced by baseline characteristics and/or might have an impact on the EMPA-induced risk reduction in kidney outcomes. Method In the EMPA-REG OUTCOME trial, patients with T2D and established CVD were treated (1:1:1) with EMPA 10 mg, 25 mg or placebo (PBO), in addition to standard of care. In this post hoc analysis, 6,668 participants who received at least one dose of study drug and had an available eGFR value at both baseline and Week 4 were categorised by initial percentage eGFR change from baseline. A multivariate logistic regression model was used to identify which baseline characteristics are predictive of an initial eGFR dip >10% in EMPA-treated participants versus PBO. Across these predictive baseline factors, we investigated the occurrence of incident or worsening nephropathy, hard kidney outcomes (defined as doubling of serum creatinine with eGFR [MDRD] ≤45 ml/min/1.73 m2 or initiation of renal replacement therapy or death from renal disease), and kidney safety (narrow standardized MedDRA query acute renal failure). The impact of an eGFR dip >10% on the risk reduction with EMPA for incident or worsening nephropathy was assessed using Cox regression analysis adjusting for such eGFR dip. Results In the EMPA-REG OUTCOME trial cohort, an initial eGFR dip of >10% from baseline at Week 4 occurred in more than twice as many participants on EMPA (28.3%) compared to PBO (13.4%). However, a more pronounced eGFR dip of >30% was uncommon, occurring in only 1.4% and 0.9%, respectively. Within the EMPA group, participants with an eGFR dip >10% were significantly older, had longer diabetes duration and showed a higher KDIGO (Kidney Disease: Improving Global Outcomes) risk category. Diuretic use and/or higher KDIGO risk category at baseline were predictive of an initial eGFR dip of >10% in EMPA vs. PBO. The average odds ratio [OR; 95% CI] for an eGFR dip >10% with EMPA was 2.7 [2.3–3.0]. In subgroups with a dipping odds ratio below vs. above that average, beneficial treatment effects with EMPA on incident or worsening nephropathy and the hard kidney outcome were consistent (panel A). Also, an eGFR dip >10% did not affect risk reduction for the primary kidney outcome (panel B). Acute renal failure rates were generally lower or similar in EMPA vs. PBO, regardless of baseline predictive factors for an eGFR dip. Conclusion T2D patients with more advanced kidney disease and/or on diuretic therapy at baseline were more likely to have an initial eGFR dip >10% with EMPA. However, EMPA treatment appeared to be safe and was associated with improved kidney outcomes, regardless of these baseline predictive factors or an initial eGFR dip >10%.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,006 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».